Improved differential evolution for dynamic optimization problems

This article reports improvements on DynDE, a approach to using Differential Evolution to solve dynamic optimization problems. Three improvements are suggested, namely favored populations, migrating individuals and a combination of these approaches. The effects of varying the change frequency, peak...

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Main Authors: du Plessis, M.C., Engelbrecht, A.P.
Format: Conference Proceeding
Language:English
Subjects:
Benchmark testing
Correlation
Evolution (biology)
Evolutionary computation
Heuristic algorithms
Optimization
Tracking
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Engelbrecht, A.P.
description This article reports improvements on DynDE, a approach to using Differential Evolution to solve dynamic optimization problems. Three improvements are suggested, namely favored populations, migrating individuals and a combination of these approaches. The effects of varying the change frequency, peak widths and the number of dimensions of the dynamic environment are investigated. Experimental results are presented that indicate that the suggested approaches constitute considerable improvements on previous research.
doi_str_mv 10.1109/CEC.2008.4630804
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subjects Benchmark testing
Correlation
Evolution (biology)
Evolutionary computation
Heuristic algorithms
Optimization
Tracking
title Improved differential evolution for dynamic optimization problems
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